{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "         0%-25%    25%-50%    50%-75%   75%-100%\n地区1    0.268660  29.571699  45.888477  24.271163\n地区2    5.212786  18.034161  65.744450  11.008603\n地区3   34.970415   5.538749  40.633790  18.857046\n地区4   17.541581   7.227040  45.632878  29.598501\n地区5    7.402781   9.480681  51.519789  31.596749\n地区6   57.604808  22.958041  17.234187   2.202964\n地区7   33.375942  17.327339  14.784840  34.511879\n地区8   12.578770  41.670907  22.728738  23.021585\n地区9    1.587675  33.432182  27.409028  37.571114\n地区10  35.336545   8.198393  21.936484  34.528579\n地区11  24.958571  29.022723  45.428270   0.590437\n地区12  12.130702  46.616220   4.834373  36.418705\n地区13  27.742144   6.047982  23.452543  42.757332\n地区14  28.239851  14.279209  23.442442  34.038499\n地区15  41.965119  39.318298  17.584638   1.131944\n地区16  31.270449   6.401149  32.761502  29.566900\n地区17  13.120609  43.212266  31.607405  12.059720\n地区18  23.017165  33.680835  19.774511  23.527489",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0%-25%</th>\n      <th>25%-50%</th>\n      <th>50%-75%</th>\n      <th>75%-100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>地区1</th>\n      <td>0.268660</td>\n      <td>29.571699</td>\n      <td>45.888477</td>\n      <td>24.271163</td>\n    </tr>\n    <tr>\n      <th>地区2</th>\n      <td>5.212786</td>\n      <td>18.034161</td>\n      <td>65.744450</td>\n      <td>11.008603</td>\n    </tr>\n    <tr>\n      <th>地区3</th>\n      <td>34.970415</td>\n      <td>5.538749</td>\n      <td>40.633790</td>\n      <td>18.857046</td>\n    </tr>\n    <tr>\n      <th>地区4</th>\n      <td>17.541581</td>\n      <td>7.227040</td>\n      <td>45.632878</td>\n      <td>29.598501</td>\n    </tr>\n    <tr>\n      <th>地区5</th>\n      <td>7.402781</td>\n      <td>9.480681</td>\n      <td>51.519789</td>\n      <td>31.596749</td>\n    </tr>\n    <tr>\n      <th>地区6</th>\n      <td>57.604808</td>\n      <td>22.958041</td>\n      <td>17.234187</td>\n      <td>2.202964</td>\n    </tr>\n    <tr>\n      <th>地区7</th>\n      <td>33.375942</td>\n      <td>17.327339</td>\n      <td>14.784840</td>\n      <td>34.511879</td>\n    </tr>\n    <tr>\n      <th>地区8</th>\n      <td>12.578770</td>\n      <td>41.670907</td>\n      <td>22.728738</td>\n      <td>23.021585</td>\n    </tr>\n    <tr>\n      <th>地区9</th>\n      <td>1.587675</td>\n      <td>33.432182</td>\n      <td>27.409028</td>\n      <td>37.571114</td>\n    </tr>\n    <tr>\n      <th>地区10</th>\n      <td>35.336545</td>\n      <td>8.198393</td>\n      <td>21.936484</td>\n      <td>34.528579</td>\n    </tr>\n    <tr>\n      <th>地区11</th>\n      <td>24.958571</td>\n      <td>29.022723</td>\n      <td>45.428270</td>\n      <td>0.590437</td>\n    </tr>\n    <tr>\n      <th>地区12</th>\n      <td>12.130702</td>\n      <td>46.616220</td>\n      <td>4.834373</td>\n      <td>36.418705</td>\n    </tr>\n    <tr>\n      <th>地区13</th>\n      <td>27.742144</td>\n      <td>6.047982</td>\n      <td>23.452543</td>\n      <td>42.757332</td>\n    </tr>\n    <tr>\n      <th>地区14</th>\n      <td>28.239851</td>\n      <td>14.279209</td>\n      <td>23.442442</td>\n      <td>34.038499</td>\n    </tr>\n    <tr>\n      <th>地区15</th>\n      <td>41.965119</td>\n      <td>39.318298</td>\n      <td>17.584638</td>\n      <td>1.131944</td>\n    </tr>\n    <tr>\n      <th>地区16</th>\n      <td>31.270449</td>\n      <td>6.401149</td>\n      <td>32.761502</td>\n      <td>29.566900</td>\n    </tr>\n    <tr>\n      <th>地区17</th>\n      <td>13.120609</td>\n      <td>43.212266</td>\n      <td>31.607405</td>\n      <td>12.059720</td>\n    </tr>\n    <tr>\n      <th>地区18</th>\n      <td>23.017165</td>\n      <td>33.680835</td>\n      <td>19.774511</td>\n      <td>23.527489</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据生成\n",
    "## 从均匀分布随机生成\n",
    "data = np.random.uniform(0,1,size=(18,4))\n",
    "## 归一化，保证每一行加起来为100\n",
    "data = data/data.sum(axis=1).reshape(-1,1)*100\n",
    "## 为数据的行和列命名\n",
    "data = pd.DataFrame(data,index=[f\"地区{i+1}\" for i in range(18)],columns=[f\"{i}%-{i+25}%\" for i in range(0,100,25)])\n",
    "data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anaconda/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/anaconda/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 20363 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/anaconda/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 22320 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/anaconda/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 21306 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/anaconda/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/anaconda/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 20363 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/anaconda/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 22320 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/anaconda/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 21306 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1008x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 指定不同类别名称，通过df的列名获取\n",
    "category_names = data.keys()\n",
    "# 指定当前用到的颜色，这里通过sns获取\n",
    "color_pad = sns.color_palette(\"hls\", 8)\n",
    "# 指定图的大小\n",
    "figsize = (14,10)\n",
    "# 指定保存路径，如果保存路径不为空，则保存图\n",
    "path = None\n",
    "\n",
    "# 取颜色盘中前n种颜色，n=类别名数量\n",
    "color_pad = color_pad[:len(category_names)]\n",
    "\n",
    "# 计算柱状图开始画的位置，后一类开始的位置应当是前一类结束的位置，如假设[1,2,3],则堆积图开始位置应当是[0,1,3]\n",
    "start = data.values.cumsum(axis=1)-data.values\n",
    "# 计算每一个柱中心位置的x坐标，用于数据标记。\n",
    "x_centers = data.values.cumsum(axis=1)-data.values/2\n",
    "\n",
    "f, ax = plt.subplots(figsize=figsize)\n",
    "# 每次遍历一个类别，并画图，其中left为柱状图的开始位置，y为不同地区或学校，x为画柱状图的数据\n",
    "for i,(category_name, color) in enumerate(zip(category_names,color_pad)):\n",
    "    sns.barplot(data=data,\n",
    "                x=category_name,\n",
    "                y=data.index,\n",
    "                left=start[:,i],\n",
    "                label=category_name,\n",
    "                color=color)\n",
    "    # x从x_center获取x坐标,标记数据为当前柱的百分比\n",
    "    for y,(x,c) in enumerate(zip(x_centers[:,i],data.values[:,i])):\n",
    "        # 为了图标的美观，如果数据大小小于行总和的1%则不进行标记\n",
    "        if c >= data.values.sum(axis=1).max()*0.01:\n",
    "            plt.text(x,y,s=\"%.1f\"%c,\n",
    "                    ha='center', va='center',\n",
    "                    fontproperties='Times New Roman', \n",
    "                    fontsize=12)\n",
    "\n",
    "\n",
    "plt.xticks(fontproperties='Times New Roman', fontsize=15)\n",
    "plt.yticks(fontproperties='SimHei',fontsize=15)\n",
    "\n",
    "# 根据数据大小调整x轴范围，根据地区或学校数量调整y轴范围\n",
    "ax.set(xlim=(0, data.values.sum(axis=1).max()),ylim=(-0.5, len(data)+0.5), ylabel=\"\")\n",
    "ax.set_xlabel('比例(%)',fontproperties='SimHei',fontsize=15)\n",
    "# 图例\n",
    "ax.legend(ncol=len(category_names),loc=\"upper center\",frameon=True,fontsize=14,prop='SimHei')\n",
    "# 隐藏左和下的坐标轴\n",
    "sns.despine(left=True, bottom=True)\n",
    "if path:\n",
    "    plt.savefig(path, bbox_inches='tight', dpi=600, pad_inches=0.1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 或者写成一个函数\n",
    "def bar_stack(data,category_names,\n",
    "            figsize=(14,10),\n",
    "            color_pad=sns.color_palette(\"hls\", 8),\n",
    "            path=None):\n",
    "    \"\"\"\n",
    "    堆积条形图\n",
    "    data: dataframe,shape=(学校或地区数量, 类别数量)\n",
    "    \"\"\"\n",
    "    \n",
    "    # 取颜色盘中前n种颜色，n=类别名数量\n",
    "    color_pad = color_pad[:len(category_names)]\n",
    "\n",
    "    # 计算柱状图开始画的位置，后一类开始的位置应当是前一类结束的位置，如假设[1,2,3],则堆积图开始位置应当是[0,1,3]\n",
    "    start = data.values.cumsum(axis=1)-data.values\n",
    "    # 计算每一个柱中心位置的x坐标，用于数据标记。\n",
    "    x_centers = data.values.cumsum(axis=1)-data.values/2\n",
    "\n",
    "    f, ax = plt.subplots(figsize=figsize)\n",
    "    # 每次遍历一个类别，并画图，其中left为柱状图的开始位置，y为不同地区或学校，x为画柱状图的数据\n",
    "    for i,(category_name, color) in enumerate(zip(category_names,color_pad)):\n",
    "        sns.barplot(data=data,\n",
    "                    x=category_name,\n",
    "                    y=data.index,\n",
    "                    left=start[:,i],\n",
    "                    label=category_name,\n",
    "                    color=color)\n",
    "        # x从x_center获取x坐标,标记数据为当前柱的百分比\n",
    "        for y,(x,c) in enumerate(zip(x_centers[:,i],data.values[:,i])):\n",
    "            # 为了图标的美观，如果数据大小小于行总和的1%则不进行标记\n",
    "            if c >= data.values.sum(axis=1).max()*0.01:\n",
    "                plt.text(x,y,s=\"%.1f\"%c,\n",
    "                        ha='center', va='center',\n",
    "                        fontproperties='Times New Roman', \n",
    "                        fontsize=12)\n",
    "\n",
    "\n",
    "    plt.xticks(fontproperties='Times New Roman', fontsize=15)\n",
    "    plt.yticks(fontproperties='SimHei',fontsize=15)\n",
    "    ax.set_xlabel('比例(%)',fontproperties='SimHei',fontsize=15)\n",
    "    # 根据数据大小调整x轴范围，根据地区或学校数量调整y轴范围\n",
    "    ax.set(xlim=(0, data.values.sum(axis=1).max()),ylim=(-0.5, len(data)+0.5), ylabel=\"\")\n",
    "    # 图例\n",
    "    ax.legend(ncol=len(category_names),loc=\"upper center\",frameon=True,fontsize=14,prop='SimHei')\n",
    "    # 隐藏左和下的坐标轴\n",
    "    sns.despine(left=True, bottom=True)\n",
    "    plt.grid(linestyle='--', alpha=0.5)\n",
    "    if path:\n",
    "        plt.savefig(path, bbox_inches='tight', dpi=600, pad_inches=0.1)\n",
    "    else:\n",
    "        return f\n",
    "bar_stack(data,data.keys())"
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.6.8 ('work')",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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